Email: abossedghs@gmail.com
- Purpose of this project is to research and apply the various machine learning regression algorithms to undertake different models.
- Identify a hedonic property price model that can be used to predict residential property prices in Orlando as a function of the physical and locational attributes of the properties.
- The data is first preprocessed and is then analyzed to summarize the main characteristics of the variables such as their correlation or any observable patterns.
- Engineered features from given data variables (Including reverse engineering x_coord and y_coord to actual measures of use, using geopy.geocoder and stateplane modules)
- Optimized Linear, Lasso, Ridge, and Extreme Gradient Boosting using RandomSearchCV to reach the best model.
- The best model will be identified by the measure of MSE (mean squared error) and its accuracy to the validation set.
- The application is able to fetch stock data from the YahooFinance API on any ticker provided by user input, as well as, storing data to a MongoDB database.
- CRUD operations to and from the MongoDB database.
- Spring Boot and Maven are used to manage dependencies and jumpstart the project.
- Integration of Service/Model/Application packages to provide clarity for class usage.
- Java OOP and encapsulation methods used.
- Usage of a MultiValueMap hashSet.
- Taking user-input from the terminal to search a ticker name and receive information on the company and its finances.
- Used Object-Oriented principles to create and use methods to perform stock analysis.
- HTML parsing to find correct tags for each specific variable.
- Perform web scraping with Python using the Beautiful Soup library.
- Looking for python related job listings on TimesJobs website.
- Includes filter to exclude a specific skill.